Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany.
Department of Medicine, Clinic III, Hematology, Oncology, Palliative Medicine, Rostock University Medical Center, Rostock, Germany.
Brief Bioinform. 2022 May 13;23(3). doi: 10.1093/bib/bbac133.
Accurate transfer learning of clinical outcomes from one cellular context to another, between cell types, developmental stages, omics modalities or species, is considered tremendously useful. When transferring a prediction task from a source domain to a target domain, what counts is the high quality of the predictions in the target domain, requiring states or processes common to both the source and the target that can be learned by the predictor reflected by shared denominators. These may form a compendium of knowledge that is learned in the source to enable predictions in the target, usually with few, if any, labeled target training samples to learn from. Transductive transfer learning refers to the learning of the predictor in the source domain, transferring its outcome label calculations to the target domain, considering the same task. Inductive transfer learning considers cases where the target predictor is performing a different yet related task as compared with the source predictor. Often, there is also a need to first map the variables in the input/feature spaces and/or the variables in the output/outcome spaces. We here discuss and juxtapose various recently published transfer learning approaches, specifically designed (or at least adaptable) to predict clinical (human in vivo) outcomes based on preclinical (mostly animal-based) molecular data, towards finding the right tool for a given task, and paving the way for a comprehensive and systematic comparison of the suitability and accuracy of transfer learning of clinical outcomes.
准确地将临床结果从一个细胞环境转移到另一个环境，从一种细胞类型转移到另一种类型，从发育阶段转移到另一个阶段，从组学模式转移到另一种模式，或者从一个物种转移到另一个物种，被认为是非常有用的。当将预测任务从源域转移到目标域时，重要的是目标域中预测的高质量，这需要源域和目标域中共同的状态或过程，这些过程可以通过预测器学习，预测器反映了共同的因素。这些可能形成一个在源域中学习的知识纲要，以实现目标域中的预测，通常只有很少（如果有的话）的目标训练样本可供学习。传导性转移学习是指在源域中学习预测器，将其结果标签计算转移到目标域，同时考虑相同的任务。归纳性转移学习考虑的是目标预测器执行的任务与源预测器不同但相关的情况。通常，还需要首先映射输入/特征空间中的变量和/或输出/结果空间中的变量。在这里，我们讨论并并列了各种最近发表的转移学习方法，这些方法专门设计（或至少可适应）用于根据临床前（主要基于动物的）分子数据预测临床（人体体内）结果，以找到适合给定任务的正确工具，并为全面系统地比较转移学习临床结果的适用性和准确性铺平道路。
